Overview

Dataset statistics

Number of variables29
Number of observations23052
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 MiB
Average record size in memory232.0 B

Variable types

DateTime1
Categorical4
Numeric24

Warnings

Recurso has a high cardinality: 64 distinct values High cardinality
Código Recurso has a high cardinality: 64 distinct values High cardinality
0.0 is highly correlated with 1.0 and 22 other fieldsHigh correlation
1.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
2.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
3.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
4.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
5.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
6.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
7.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
8.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
9.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
10.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
11.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
12.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
13.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
14.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
15.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
16.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
17.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
18.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
19.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
20.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
21.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
22.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
23.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
0.0 is highly correlated with 1.0 and 22 other fieldsHigh correlation
1.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
2.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
3.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
4.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
5.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
6.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
7.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
8.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
9.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
10.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
11.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
12.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
13.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
14.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
15.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
16.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
17.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
18.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
19.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
20.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
21.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
22.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
23.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
0.0 is highly correlated with 1.0 and 22 other fieldsHigh correlation
1.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
2.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
3.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
4.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
5.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
6.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
7.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
8.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
9.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
10.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
11.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
12.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
13.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
14.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
15.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
16.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
17.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
18.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
19.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
20.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
21.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
22.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
23.0 is highly correlated with 0.0 and 22 other fieldsHigh correlation
21.0 is highly correlated with 4.0 and 26 other fieldsHigh correlation
4.0 is highly correlated with 21.0 and 25 other fieldsHigh correlation
18.0 is highly correlated with 21.0 and 26 other fieldsHigh correlation
5.0 is highly correlated with 21.0 and 25 other fieldsHigh correlation
6.0 is highly correlated with 21.0 and 25 other fieldsHigh correlation
19.0 is highly correlated with 21.0 and 26 other fieldsHigh correlation
2.0 is highly correlated with 21.0 and 25 other fieldsHigh correlation
7.0 is highly correlated with 21.0 and 25 other fieldsHigh correlation
15.0 is highly correlated with 21.0 and 25 other fieldsHigh correlation
16.0 is highly correlated with 21.0 and 25 other fieldsHigh correlation
11.0 is highly correlated with 21.0 and 25 other fieldsHigh correlation
14.0 is highly correlated with 21.0 and 25 other fieldsHigh correlation
9.0 is highly correlated with 21.0 and 25 other fieldsHigh correlation
20.0 is highly correlated with 21.0 and 26 other fieldsHigh correlation
Código Agente is highly correlated with 21.0 and 26 other fieldsHigh correlation
17.0 is highly correlated with 21.0 and 26 other fieldsHigh correlation
3.0 is highly correlated with 21.0 and 25 other fieldsHigh correlation
Recurso is highly correlated with 21.0 and 26 other fieldsHigh correlation
Tipo Generación is highly correlated with 21.0 and 7 other fieldsHigh correlation
13.0 is highly correlated with 21.0 and 25 other fieldsHigh correlation
8.0 is highly correlated with 21.0 and 25 other fieldsHigh correlation
10.0 is highly correlated with 21.0 and 25 other fieldsHigh correlation
22.0 is highly correlated with 21.0 and 25 other fieldsHigh correlation
1.0 is highly correlated with 21.0 and 25 other fieldsHigh correlation
12.0 is highly correlated with 21.0 and 25 other fieldsHigh correlation
23.0 is highly correlated with 21.0 and 25 other fieldsHigh correlation
0.0 is highly correlated with 21.0 and 25 other fieldsHigh correlation
Código Recurso is highly correlated with 21.0 and 26 other fieldsHigh correlation
Código Agente is highly correlated with Tipo Generación and 2 other fieldsHigh correlation
Tipo Generación is highly correlated with Código Agente and 2 other fieldsHigh correlation
Recurso is highly correlated with Código Agente and 2 other fieldsHigh correlation
Código Recurso is highly correlated with Código Agente and 2 other fieldsHigh correlation
0.0 has 2331 (10.1%) zeros Zeros
1.0 has 2359 (10.2%) zeros Zeros
2.0 has 2364 (10.3%) zeros Zeros
3.0 has 2369 (10.3%) zeros Zeros
4.0 has 2376 (10.3%) zeros Zeros
5.0 has 2349 (10.2%) zeros Zeros
6.0 has 2233 (9.7%) zeros Zeros
7.0 has 2111 (9.2%) zeros Zeros
8.0 has 2031 (8.8%) zeros Zeros
9.0 has 2009 (8.7%) zeros Zeros
10.0 has 2007 (8.7%) zeros Zeros
11.0 has 2018 (8.8%) zeros Zeros
12.0 has 2035 (8.8%) zeros Zeros
13.0 has 2257 (9.8%) zeros Zeros
14.0 has 2272 (9.9%) zeros Zeros
15.0 has 2263 (9.8%) zeros Zeros
16.0 has 2096 (9.1%) zeros Zeros
17.0 has 1982 (8.6%) zeros Zeros
18.0 has 2001 (8.7%) zeros Zeros
19.0 has 2005 (8.7%) zeros Zeros
20.0 has 2001 (8.7%) zeros Zeros
21.0 has 2040 (8.8%) zeros Zeros
22.0 has 2167 (9.4%) zeros Zeros
23.0 has 2288 (9.9%) zeros Zeros

Reproduction

Analysis started2021-07-02 01:50:34.342529
Analysis finished2021-07-02 01:52:42.053575
Duration2 minutes and 7.71 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Fecha
Date

Distinct365
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size180.2 KiB
Minimum2019-01-01 00:00:00
Maximum2019-12-31 00:00:00
2021-07-01T20:52:42.303328image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:42.553762image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Recurso
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct64
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size180.2 KiB
JAGUAS
 
365
TERMOCANDELARIA 2
 
365
ZIPAEMG 4
 
365
CARTAGENA 2
 
365
TASAJERO 2
 
365
Other values (59)
21227 

Length

Max length21
Median length9
Mean length9.746095783
Min length4

Characters and Unicode

Total characters224667
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowALBAN
2nd rowAMOYA LA ESPERANZA
3rd rowBETANIA
4th rowCALIMA
5th rowCARLOS LLERAS

Common Values

ValueCountFrequency (%)
JAGUAS365
 
1.6%
TERMOCANDELARIA 2365
 
1.6%
ZIPAEMG 4365
 
1.6%
CARTAGENA 2365
 
1.6%
TASAJERO 2365
 
1.6%
PROELECTRICA 1365
 
1.6%
BETANIA365
 
1.6%
DARIO VALENCIA SAMPER365
 
1.6%
PAIPA 4365
 
1.6%
PRADO365
 
1.6%
Other values (54)19402
84.2%

Length

2021-07-01T20:52:43.028848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14015
 
10.0%
22920
 
7.3%
31825
 
4.6%
paipa1460
 
3.7%
zipaemg1460
 
3.7%
san1095
 
2.7%
41095
 
2.7%
cartagena1095
 
2.7%
proelectrica730
 
1.8%
el730
 
1.8%
Other values (57)23533
58.9%

Most occurring characters

ValueCountFrequency (%)
A36980
16.5%
E19883
 
8.8%
R18615
 
8.3%
16906
 
7.5%
I14293
 
6.4%
O13140
 
5.8%
L12468
 
5.5%
C11008
 
4.9%
T9124
 
4.1%
S8875
 
4.0%
Other values (19)63375
28.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter196446
87.4%
Space Separator16906
 
7.5%
Decimal Number11315
 
5.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A36980
18.8%
E19883
10.1%
R18615
9.5%
I14293
 
7.3%
O13140
 
6.7%
L12468
 
6.3%
C11008
 
5.6%
T9124
 
4.6%
S8875
 
4.5%
P8760
 
4.5%
Other values (13)43300
22.0%
Decimal Number
ValueCountFrequency (%)
14015
35.5%
23285
29.0%
32190
19.4%
41460
 
12.9%
5365
 
3.2%
Space Separator
ValueCountFrequency (%)
16906
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin196446
87.4%
Common28221
 
12.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A36980
18.8%
E19883
10.1%
R18615
9.5%
I14293
 
7.3%
O13140
 
6.7%
L12468
 
6.3%
C11008
 
5.6%
T9124
 
4.6%
S8875
 
4.5%
P8760
 
4.5%
Other values (13)43300
22.0%
Common
ValueCountFrequency (%)
16906
59.9%
14015
 
14.2%
23285
 
11.6%
32190
 
7.8%
41460
 
5.2%
5365
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII224667
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A36980
16.5%
E19883
 
8.8%
R18615
 
8.3%
16906
 
7.5%
I14293
 
6.4%
O13140
 
5.8%
L12468
 
5.5%
C11008
 
4.9%
T9124
 
4.1%
S8875
 
4.0%
Other values (19)63375
28.2%

Código Recurso
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct64
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size180.2 KiB
GTRG
 
365
TEC1
 
365
CTG3
 
365
CTG1
 
365
EPFV
 
365
Other values (59)
21227 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters92208
Distinct characters29
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowALBG
2nd rowMOY1
3rd rowCHBG
4th rowCLMG
5th rowCLL1

Common Values

ValueCountFrequency (%)
GTRG365
 
1.6%
TEC1365
 
1.6%
CTG3365
 
1.6%
CTG1365
 
1.6%
EPFV365
 
1.6%
TRN1365
 
1.6%
TSR1365
 
1.6%
SNCR365
 
1.6%
PPA1365
 
1.6%
CHBG365
 
1.6%
Other values (54)19402
84.2%

Length

2021-07-01T20:52:43.403817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tfl1365
 
1.6%
smi1365
 
1.6%
sog1365
 
1.6%
gtrg365
 
1.6%
gtpe365
 
1.6%
epfv365
 
1.6%
chbg365
 
1.6%
trn1365
 
1.6%
zpa2365
 
1.6%
qui1365
 
1.6%
Other values (54)19402
84.2%

Most occurring characters

ValueCountFrequency (%)
T9123
 
9.9%
18030
 
8.7%
P8030
 
8.7%
G7665
 
8.3%
C5840
 
6.3%
R5840
 
6.3%
S5109
 
5.5%
L4380
 
4.8%
A4015
 
4.4%
24015
 
4.4%
Other values (19)30161
32.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter75783
82.2%
Decimal Number16425
 
17.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T9123
12.0%
P8030
 
10.6%
G7665
 
10.1%
C5840
 
7.7%
R5840
 
7.7%
S5109
 
6.7%
L4380
 
5.8%
A4015
 
5.3%
M2613
 
3.4%
E2555
 
3.4%
Other values (14)20613
27.2%
Decimal Number
ValueCountFrequency (%)
18030
48.9%
24015
24.4%
32555
 
15.6%
41460
 
8.9%
5365
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Latin75783
82.2%
Common16425
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
T9123
12.0%
P8030
 
10.6%
G7665
 
10.1%
C5840
 
7.7%
R5840
 
7.7%
S5109
 
6.7%
L4380
 
5.8%
A4015
 
5.3%
M2613
 
3.4%
E2555
 
3.4%
Other values (14)20613
27.2%
Common
ValueCountFrequency (%)
18030
48.9%
24015
24.4%
32555
 
15.6%
41460
 
8.9%
5365
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII92208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T9123
 
9.9%
18030
 
8.7%
P8030
 
8.7%
G7665
 
8.3%
C5840
 
6.3%
R5840
 
6.3%
S5109
 
5.5%
L4380
 
4.8%
A4015
 
4.4%
24015
 
4.4%
Other values (19)30161
32.7%

Tipo Generación
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size180.2 KiB
TERMICA
12409 
HIDRAULICA
10278 
SOLAR
 
365

Length

Max length10
Median length7
Mean length8.305917057
Min length5

Characters and Unicode

Total characters191468
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHIDRAULICA
2nd rowHIDRAULICA
3rd rowHIDRAULICA
4th rowHIDRAULICA
5th rowHIDRAULICA

Common Values

ValueCountFrequency (%)
TERMICA12409
53.8%
HIDRAULICA10278
44.6%
SOLAR365
 
1.6%

Length

2021-07-01T20:52:43.703774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-01T20:52:43.808743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
termica12409
53.8%
hidraulica10278
44.6%
solar365
 
1.6%

Most occurring characters

ValueCountFrequency (%)
A33330
17.4%
I32965
17.2%
R23052
12.0%
C22687
11.8%
T12409
 
6.5%
E12409
 
6.5%
M12409
 
6.5%
L10643
 
5.6%
H10278
 
5.4%
D10278
 
5.4%
Other values (3)11008
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter191468
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A33330
17.4%
I32965
17.2%
R23052
12.0%
C22687
11.8%
T12409
 
6.5%
E12409
 
6.5%
M12409
 
6.5%
L10643
 
5.6%
H10278
 
5.4%
D10278
 
5.4%
Other values (3)11008
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Latin191468
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A33330
17.4%
I32965
17.2%
R23052
12.0%
C22687
11.8%
T12409
 
6.5%
E12409
 
6.5%
M12409
 
6.5%
L10643
 
5.6%
H10278
 
5.4%
D10278
 
5.4%
Other values (3)11008
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII191468
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A33330
17.4%
I32965
17.2%
R23052
12.0%
C22687
11.8%
T12409
 
6.5%
E12409
 
6.5%
M12409
 
6.5%
L10643
 
5.6%
H10278
 
5.4%
D10278
 
5.4%
Other values (3)11008
 
5.7%

Código Agente
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size180.2 KiB
ENDG
5110 
EPMG
3650 
ISGG
2190 
EPSG
2190 
GECG
1460 
Other values (16)
8452 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters92208
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEPSG
2nd rowISGG
3rd rowENDG
4th rowEPSG
5th rowHDPG

Common Values

ValueCountFrequency (%)
ENDG5110
22.2%
EPMG3650
15.8%
ISGG2190
9.5%
EPSG2190
9.5%
GECG1460
 
6.3%
HIMG1102
 
4.8%
TBSG1094
 
4.7%
TMFG730
 
3.2%
PRIG730
 
3.2%
TCIG730
 
3.2%
Other values (11)4066
17.6%

Length

2021-07-01T20:52:44.108590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
endg5110
22.2%
epmg3650
15.8%
isgg2190
9.5%
epsg2190
9.5%
gecg1460
 
6.3%
himg1102
 
4.8%
tbsg1094
 
4.7%
tmfg730
 
3.2%
tcig730
 
3.2%
prig730
 
3.2%
Other values (11)4066
17.6%

Most occurring characters

ValueCountFrequency (%)
G26702
29.0%
E13505
14.6%
P7358
 
8.0%
M7307
 
7.9%
S6197
 
6.7%
D5533
 
6.0%
N5475
 
5.9%
I4752
 
5.2%
T4744
 
5.1%
C3278
 
3.6%
Other values (9)7357
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter92208
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G26702
29.0%
E13505
14.6%
P7358
 
8.0%
M7307
 
7.9%
S6197
 
6.7%
D5533
 
6.0%
N5475
 
5.9%
I4752
 
5.2%
T4744
 
5.1%
C3278
 
3.6%
Other values (9)7357
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Latin92208
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G26702
29.0%
E13505
14.6%
P7358
 
8.0%
M7307
 
7.9%
S6197
 
6.7%
D5533
 
6.0%
N5475
 
5.9%
I4752
 
5.2%
T4744
 
5.1%
C3278
 
3.6%
Other values (9)7357
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII92208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G26702
29.0%
E13505
14.6%
P7358
 
8.0%
M7307
 
7.9%
S6197
 
6.7%
D5533
 
6.0%
N5475
 
5.9%
I4752
 
5.2%
T4744
 
5.1%
C3278
 
3.6%
Other values (9)7357
 
8.0%

0.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct669
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214764.4822
Minimum0
Maximum1245000
Zeros2331
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:44.308496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145000
median138000
Q3298000
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)253000

Descriptive statistics

Standard deviation257452.1266
Coefficient of variation (CV)1.198764917
Kurtosis3.680592248
Mean214764.4822
Median Absolute Deviation (MAD)93000
Skewness1.935301689
Sum4950750843
Variance6.628159752 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:44.553799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02331
 
10.1%
45000968
 
4.2%
170000686
 
3.0%
30000676
 
2.9%
160000608
 
2.6%
157000592
 
2.6%
60000583
 
2.5%
63000517
 
2.2%
72000445
 
1.9%
62000376
 
1.6%
Other values (659)15270
66.2%
ValueCountFrequency (%)
02331
10.1%
15001
 
< 0.1%
18001
 
< 0.1%
20001
 
< 0.1%
35002
 
< 0.1%
37501
 
< 0.1%
56001
 
< 0.1%
60001
 
< 0.1%
60661
 
< 0.1%
61331
 
< 0.1%
ValueCountFrequency (%)
124500045
 
0.2%
1240000169
0.7%
123500056
 
0.2%
12348321
 
< 0.1%
123000012
 
0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%
12080001
 
< 0.1%

1.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct552
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214641.8836
Minimum0
Maximum1245000
Zeros2359
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:44.788520image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145000
median138000
Q3298000
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)253000

Descriptive statistics

Standard deviation257469.7146
Coefficient of variation (CV)1.199531565
Kurtosis3.68542455
Mean214641.8836
Median Absolute Deviation (MAD)93000
Skewness1.936274213
Sum4947924701
Variance6.629065391 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:45.008391image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02359
 
10.2%
45000970
 
4.2%
170000685
 
3.0%
30000674
 
2.9%
160000606
 
2.6%
157000592
 
2.6%
60000578
 
2.5%
63000517
 
2.2%
72000445
 
1.9%
62000375
 
1.6%
Other values (542)15251
66.2%
ValueCountFrequency (%)
02359
10.2%
5001
 
< 0.1%
34501
 
< 0.1%
36661
 
< 0.1%
50003
 
< 0.1%
54161
 
< 0.1%
700022
 
0.1%
80007
 
< 0.1%
900021
 
0.1%
96001
 
< 0.1%
ValueCountFrequency (%)
124500044
 
0.2%
1240000172
0.7%
123500056
 
0.2%
123000012
 
0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%
12080001
 
< 0.1%
12050001
 
< 0.1%

2.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct553
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214641.5549
Minimum0
Maximum1245000
Zeros2364
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:45.239198image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145000
median138000
Q3298000
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)253000

Descriptive statistics

Standard deviation257649.7668
Coefficient of variation (CV)1.200372253
Kurtosis3.686564075
Mean214641.5549
Median Absolute Deviation (MAD)93000
Skewness1.936673381
Sum4947917124
Variance6.638340233 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:45.454623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02364
 
10.3%
45000970
 
4.2%
170000686
 
3.0%
30000677
 
2.9%
160000606
 
2.6%
157000592
 
2.6%
60000577
 
2.5%
63000513
 
2.2%
72000443
 
1.9%
62000378
 
1.6%
Other values (543)15246
66.1%
ValueCountFrequency (%)
02364
10.3%
36161
 
< 0.1%
44001
 
< 0.1%
50002
 
< 0.1%
66661
 
< 0.1%
700022
 
0.1%
73331
 
< 0.1%
80006
 
< 0.1%
900021
 
0.1%
1000018
 
0.1%
ValueCountFrequency (%)
124500046
 
0.2%
1240000172
0.7%
123500056
 
0.2%
123000012
 
0.1%
12250001
 
< 0.1%
12242501
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%
12080001
 
< 0.1%

3.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct538
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214625.2749
Minimum0
Maximum1245000
Zeros2369
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:45.663277image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145000
median138000
Q3298000
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)253000

Descriptive statistics

Standard deviation257652.1859
Coefficient of variation (CV)1.200474576
Kurtosis3.687697056
Mean214625.2749
Median Absolute Deviation (MAD)93000
Skewness1.936902033
Sum4947541838
Variance6.638464891 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:45.888542image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02369
 
10.3%
45000970
 
4.2%
170000685
 
3.0%
30000676
 
2.9%
160000607
 
2.6%
157000592
 
2.6%
60000572
 
2.5%
63000511
 
2.2%
72000445
 
1.9%
62000377
 
1.6%
Other values (528)15248
66.1%
ValueCountFrequency (%)
02369
10.3%
1331
 
< 0.1%
13331
 
< 0.1%
32001
 
< 0.1%
46661
 
< 0.1%
50002
 
< 0.1%
60001
 
< 0.1%
700021
 
0.1%
80005
 
< 0.1%
83331
 
< 0.1%
ValueCountFrequency (%)
124500046
 
0.2%
1240000172
0.7%
123500056
 
0.2%
123000012
 
0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12115831
 
< 0.1%
12100001
 
< 0.1%
12080001
 
< 0.1%

4.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct538
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214485.4619
Minimum0
Maximum1245000
Zeros2376
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:46.103411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145000
median138000
Q3296000
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)251000

Descriptive statistics

Standard deviation257678.6588
Coefficient of variation (CV)1.201380534
Kurtosis3.695869758
Mean214485.4619
Median Absolute Deviation (MAD)93000
Skewness1.938885583
Sum4944318868
Variance6.639829119 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:46.323745image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02376
 
10.3%
45000969
 
4.2%
170000686
 
3.0%
30000675
 
2.9%
160000608
 
2.6%
157000592
 
2.6%
60000570
 
2.5%
63000517
 
2.2%
72000445
 
1.9%
62000378
 
1.6%
Other values (528)15236
66.1%
ValueCountFrequency (%)
02376
10.3%
4001
 
< 0.1%
36001
 
< 0.1%
50001
 
< 0.1%
54001
 
< 0.1%
60001
 
< 0.1%
63001
 
< 0.1%
700021
 
0.1%
80005
 
< 0.1%
900020
 
0.1%
ValueCountFrequency (%)
124500046
 
0.2%
1240000173
0.8%
123500056
 
0.2%
123000012
 
0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%
12080001
 
< 0.1%
12050001
 
< 0.1%

5.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct581
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214329.6277
Minimum0
Maximum1245000
Zeros2349
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:46.543803image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145000
median138000
Q3294000
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)249000

Descriptive statistics

Standard deviation257620.4772
Coefficient of variation (CV)1.201982572
Kurtosis3.712213613
Mean214329.6277
Median Absolute Deviation (MAD)93000
Skewness1.94250636
Sum4940726578
Variance6.636831029 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:46.763721image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02349
 
10.2%
45000956
 
4.1%
170000687
 
3.0%
30000683
 
3.0%
160000607
 
2.6%
157000592
 
2.6%
60000561
 
2.4%
63000517
 
2.2%
72000446
 
1.9%
62000378
 
1.6%
Other values (571)15276
66.3%
ValueCountFrequency (%)
02349
10.2%
331
 
< 0.1%
832
 
< 0.1%
4161
 
< 0.1%
6661
 
< 0.1%
7508
 
< 0.1%
8161
 
< 0.1%
11661
 
< 0.1%
13001
 
< 0.1%
15003
 
< 0.1%
ValueCountFrequency (%)
124500046
 
0.2%
1240000175
0.8%
123500056
 
0.2%
123000012
 
0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%
12080001
 
< 0.1%
12050001
 
< 0.1%

6.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct660
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214547.2422
Minimum0
Maximum1245000
Zeros2233
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:46.983287image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145000
median138000
Q3294000
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)249000

Descriptive statistics

Standard deviation257684.2348
Coefficient of variation (CV)1.201060578
Kurtosis3.721934782
Mean214547.2422
Median Absolute Deviation (MAD)93000
Skewness1.944699797
Sum4945743027
Variance6.640116484 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:47.263276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02233
 
9.7%
45000962
 
4.2%
170000685
 
3.0%
30000683
 
3.0%
160000609
 
2.6%
157000592
 
2.6%
60000562
 
2.4%
63000514
 
2.2%
72000444
 
1.9%
62000399
 
1.7%
Other values (650)15369
66.7%
ValueCountFrequency (%)
02233
9.7%
75015
 
0.1%
10001
 
< 0.1%
12001
 
< 0.1%
150015
 
0.1%
22503
 
< 0.1%
24001
 
< 0.1%
25001
 
< 0.1%
25501
 
< 0.1%
26001
 
< 0.1%
ValueCountFrequency (%)
124500046
 
0.2%
1240000174
0.8%
123500056
 
0.2%
123000012
 
0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%
12080001
 
< 0.1%
12050001
 
< 0.1%

7.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct669
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215317.5621
Minimum0
Maximum1245000
Zeros2111
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:47.538601image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145000
median138000
Q3292000
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)247000

Descriptive statistics

Standard deviation258648.0974
Coefficient of variation (CV)1.201240135
Kurtosis3.793608631
Mean215317.5621
Median Absolute Deviation (MAD)93000
Skewness1.962772535
Sum4963500441
Variance6.689883829 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:47.843542image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02111
 
9.2%
45000992
 
4.3%
30000687
 
3.0%
170000678
 
2.9%
160000608
 
2.6%
157000591
 
2.6%
60000562
 
2.4%
63000511
 
2.2%
72000443
 
1.9%
62000394
 
1.7%
Other values (659)15475
67.1%
ValueCountFrequency (%)
02111
9.2%
7506
 
< 0.1%
11501
 
< 0.1%
15007
 
< 0.1%
22505
 
< 0.1%
25001
 
< 0.1%
30004
 
< 0.1%
33332
 
< 0.1%
37502
 
< 0.1%
45002
 
< 0.1%
ValueCountFrequency (%)
124500046
 
0.2%
1240000191
0.8%
123500055
 
0.2%
123000011
 
< 0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12124001
 
< 0.1%
12100001
 
< 0.1%
12050001
 
< 0.1%

8.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct645
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215982.7397
Minimum0
Maximum1245000
Zeros2031
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:48.183409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145000
median138000
Q3292000
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)247000

Descriptive statistics

Standard deviation259352.8658
Coefficient of variation (CV)1.200803667
Kurtosis3.84421061
Mean215982.7397
Median Absolute Deviation (MAD)93000
Skewness1.974409034
Sum4978834115
Variance6.726390898 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:48.568551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02031
 
8.8%
450001037
 
4.5%
30000675
 
2.9%
170000674
 
2.9%
160000615
 
2.7%
157000592
 
2.6%
60000561
 
2.4%
63000514
 
2.2%
72000445
 
1.9%
62000395
 
1.7%
Other values (635)15513
67.3%
ValueCountFrequency (%)
02031
8.8%
3831
 
< 0.1%
7503
 
< 0.1%
20831
 
< 0.1%
22502
 
< 0.1%
30001
 
< 0.1%
44001
 
< 0.1%
60001
 
< 0.1%
67501
 
< 0.1%
700021
 
0.1%
ValueCountFrequency (%)
124500046
 
0.2%
1240000209
0.9%
123500055
 
0.2%
12320002
 
< 0.1%
123000011
 
< 0.1%
12270831
 
< 0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%

9.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct627
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216341.4161
Minimum0
Maximum1245000
Zeros2009
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:48.888715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145000
median138000
Q3292000
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)247000

Descriptive statistics

Standard deviation259653.936
Coefficient of variation (CV)1.200204476
Kurtosis3.855688771
Mean216341.4161
Median Absolute Deviation (MAD)93000
Skewness1.977714354
Sum4987102323
Variance6.742016648 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:49.248415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02009
 
8.7%
450001080
 
4.7%
170000681
 
3.0%
160000611
 
2.7%
157000592
 
2.6%
60000562
 
2.4%
30000534
 
2.3%
63000518
 
2.2%
72000444
 
1.9%
62000394
 
1.7%
Other values (617)15627
67.8%
ValueCountFrequency (%)
02009
8.7%
4001
 
< 0.1%
6001
 
< 0.1%
6661
 
< 0.1%
10001
 
< 0.1%
21001
 
< 0.1%
23331
 
< 0.1%
26001
 
< 0.1%
38331
 
< 0.1%
49501
 
< 0.1%
ValueCountFrequency (%)
124500046
 
0.2%
1240000217
0.9%
123500055
 
0.2%
12320002
 
< 0.1%
123000011
 
< 0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%
12050001
 
< 0.1%

10.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct602
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217013.7593
Minimum0
Maximum1246000
Zeros2007
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:49.623677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q146000
median138000
Q3296000
95-th percentile791000
Maximum1246000
Range1246000
Interquartile range (IQR)250000

Descriptive statistics

Standard deviation260911.2342
Coefficient of variation (CV)1.202279685
Kurtosis3.859947692
Mean217013.7593
Median Absolute Deviation (MAD)93000
Skewness1.980893834
Sum5002601179
Variance6.807467216 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:50.003658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02007
 
8.7%
450001084
 
4.7%
170000673
 
2.9%
160000612
 
2.7%
157000595
 
2.6%
60000560
 
2.4%
63000519
 
2.3%
72000447
 
1.9%
30000413
 
1.8%
62000401
 
1.7%
Other values (592)15741
68.3%
ValueCountFrequency (%)
02007
8.7%
1661
 
< 0.1%
10661
 
< 0.1%
12001
 
< 0.1%
14331
 
< 0.1%
15001
 
< 0.1%
20001
 
< 0.1%
24161
 
< 0.1%
26661
 
< 0.1%
28331
 
< 0.1%
ValueCountFrequency (%)
12460001
 
< 0.1%
124500045
 
0.2%
1240000229
1.0%
123500055
 
0.2%
12320002
 
< 0.1%
123000011
 
< 0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%

11.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct624
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217104.3378
Minimum0
Maximum1246000
Zeros2018
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:50.353649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145000
median138000
Q3296000
95-th percentile791000
Maximum1246000
Range1246000
Interquartile range (IQR)251000

Descriptive statistics

Standard deviation261288.9
Coefficient of variation (CV)1.203517638
Kurtosis3.853974337
Mean217104.3378
Median Absolute Deviation (MAD)93000
Skewness1.980007684
Sum5004689196
Variance6.827188925 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:50.743442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02018
 
8.8%
450001075
 
4.7%
170000673
 
2.9%
160000609
 
2.6%
157000594
 
2.6%
60000558
 
2.4%
63000520
 
2.3%
72000445
 
1.9%
62000404
 
1.8%
138000371
 
1.6%
Other values (614)15785
68.5%
ValueCountFrequency (%)
02018
8.8%
12001
 
< 0.1%
14661
 
< 0.1%
16501
 
< 0.1%
22502
 
< 0.1%
29161
 
< 0.1%
30001
 
< 0.1%
42161
 
< 0.1%
50002
 
< 0.1%
52501
 
< 0.1%
ValueCountFrequency (%)
12460001
 
< 0.1%
124500044
 
0.2%
1240000235
1.0%
123500055
 
0.2%
12320002
 
< 0.1%
123000011
 
< 0.1%
12270831
 
< 0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%

12.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct609
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216696.1832
Minimum0
Maximum1246000
Zeros2035
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:51.093303image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145000
median138000
Q3298000
95-th percentile791000
Maximum1246000
Range1246000
Interquartile range (IQR)253000

Descriptive statistics

Standard deviation261645.9841
Coefficient of variation (CV)1.207432361
Kurtosis3.807986006
Mean216696.1832
Median Absolute Deviation (MAD)93000
Skewness1.969582681
Sum4995280414
Variance6.8458621 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:51.518473image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02035
 
8.8%
45000995
 
4.3%
170000678
 
2.9%
160000607
 
2.6%
157000594
 
2.6%
60000559
 
2.4%
63000517
 
2.2%
72000441
 
1.9%
62000405
 
1.8%
138000372
 
1.6%
Other values (599)15849
68.8%
ValueCountFrequency (%)
02035
8.8%
7504
 
< 0.1%
14001
 
< 0.1%
14501
 
< 0.1%
15007
 
< 0.1%
22505
 
< 0.1%
300012
 
0.1%
36001
 
< 0.1%
375010
 
< 0.1%
450015
 
0.1%
ValueCountFrequency (%)
12460001
 
< 0.1%
124500045
 
0.2%
1240000229
1.0%
123500055
 
0.2%
12320002
 
< 0.1%
123000011
 
< 0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%

13.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct594
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216562.4297
Minimum0
Maximum1245000
Zeros2257
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:51.813701image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145000
median138000
Q3296000
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)251000

Descriptive statistics

Standard deviation262013.6188
Coefficient of variation (CV)1.209875689
Kurtosis3.81392067
Mean216562.4297
Median Absolute Deviation (MAD)93000
Skewness1.970762774
Sum4992197129
Variance6.865113643 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:52.038371image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02257
 
9.8%
45000988
 
4.3%
170000682
 
3.0%
160000606
 
2.6%
157000592
 
2.6%
60000563
 
2.4%
63000514
 
2.2%
72000442
 
1.9%
62000402
 
1.7%
138000373
 
1.6%
Other values (584)15633
67.8%
ValueCountFrequency (%)
02257
9.8%
2501
 
< 0.1%
7501
 
< 0.1%
11661
 
< 0.1%
15002
 
< 0.1%
18331
 
< 0.1%
19831
 
< 0.1%
22501
 
< 0.1%
30002
 
< 0.1%
37501
 
< 0.1%
ValueCountFrequency (%)
124500046
 
0.2%
1240000233
1.0%
123500056
 
0.2%
12320002
 
< 0.1%
123000011
 
< 0.1%
12250001
 
< 0.1%
12245001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%

14.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct602
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216555.7156
Minimum0
Maximum1245000
Zeros2272
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:52.258659image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145000
median138000
Q3296000
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)251000

Descriptive statistics

Standard deviation262187.2368
Coefficient of variation (CV)1.210714924
Kurtosis3.813551178
Mean216555.7156
Median Absolute Deviation (MAD)93000
Skewness1.971143716
Sum4992042356
Variance6.874214713 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:52.473705image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02272
 
9.9%
45000989
 
4.3%
170000681
 
3.0%
160000607
 
2.6%
157000593
 
2.6%
60000562
 
2.4%
63000512
 
2.2%
72000441
 
1.9%
62000401
 
1.7%
138000373
 
1.6%
Other values (592)15621
67.8%
ValueCountFrequency (%)
02272
9.9%
29331
 
< 0.1%
30002
 
< 0.1%
35001
 
< 0.1%
38331
 
< 0.1%
38661
 
< 0.1%
40001
 
< 0.1%
44001
 
< 0.1%
46661
 
< 0.1%
50002
 
< 0.1%
ValueCountFrequency (%)
124500046
 
0.2%
1240000235
1.0%
123500057
 
0.2%
12320002
 
< 0.1%
123000011
 
< 0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%
12050001
 
< 0.1%

15.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct623
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216795.2585
Minimum0
Maximum1245000
Zeros2263
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:52.673316image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145000
median138000
Q3296000
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)251000

Descriptive statistics

Standard deviation262401.9911
Coefficient of variation (CV)1.210367759
Kurtosis3.800442664
Mean216795.2585
Median Absolute Deviation (MAD)93000
Skewness1.968386395
Sum4997564298
Variance6.885480494 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:52.883637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02263
 
9.8%
45000985
 
4.3%
170000683
 
3.0%
160000605
 
2.6%
157000594
 
2.6%
60000563
 
2.4%
63000513
 
2.2%
72000441
 
1.9%
62000401
 
1.7%
138000372
 
1.6%
Other values (613)15632
67.8%
ValueCountFrequency (%)
02263
9.8%
1501
 
< 0.1%
3001
 
< 0.1%
5001
 
< 0.1%
7505
 
< 0.1%
8331
 
< 0.1%
11661
 
< 0.1%
15009
 
< 0.1%
22502
 
< 0.1%
30002
 
< 0.1%
ValueCountFrequency (%)
124500046
 
0.2%
1240000234
1.0%
123500056
 
0.2%
12348331
 
< 0.1%
12320003
 
< 0.1%
123000011
 
< 0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%

16.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct720
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217582.6798
Minimum0
Maximum1245000
Zeros2096
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:53.083519image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145000
median138000
Q3298500
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)253500

Descriptive statistics

Standard deviation262952.9307
Coefficient of variation (CV)1.208519588
Kurtosis3.794238722
Mean217582.6798
Median Absolute Deviation (MAD)93000
Skewness1.968801307
Sum5015715935
Variance6.914424375 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:53.308350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02096
 
9.1%
45000989
 
4.3%
170000679
 
2.9%
160000605
 
2.6%
157000594
 
2.6%
60000560
 
2.4%
63000509
 
2.2%
72000441
 
1.9%
62000400
 
1.7%
30000387
 
1.7%
Other values (710)15792
68.5%
ValueCountFrequency (%)
02096
9.1%
1501
 
< 0.1%
1662
 
< 0.1%
6001
 
< 0.1%
75022
 
0.1%
150012
 
0.1%
15331
 
< 0.1%
17501
 
< 0.1%
20002
 
< 0.1%
21001
 
< 0.1%
ValueCountFrequency (%)
124500048
 
0.2%
1240000237
1.0%
123500056
 
0.2%
12320003
 
< 0.1%
123000011
 
< 0.1%
12270831
 
< 0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%

17.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct721
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219069.9237
Minimum0
Maximum1245000
Zeros1982
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:53.523842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145000
median138000
Q3300000
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)255000

Descriptive statistics

Standard deviation263522.7868
Coefficient of variation (CV)1.202916321
Kurtosis3.790554953
Mean219069.9237
Median Absolute Deviation (MAD)93000
Skewness1.969918343
Sum5049999882
Variance6.944425915 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:53.753639image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01982
 
8.6%
450001044
 
4.5%
170000684
 
3.0%
160000604
 
2.6%
30000595
 
2.6%
157000593
 
2.6%
60000561
 
2.4%
63000512
 
2.2%
72000447
 
1.9%
62000385
 
1.7%
Other values (711)15645
67.9%
ValueCountFrequency (%)
01982
8.6%
6001
 
< 0.1%
7331
 
< 0.1%
7502
 
< 0.1%
8001
 
< 0.1%
9501
 
< 0.1%
9661
 
< 0.1%
10001
 
< 0.1%
11001
 
< 0.1%
11501
 
< 0.1%
ValueCountFrequency (%)
124500048
 
0.2%
1240000247
1.1%
123500056
 
0.2%
12320003
 
< 0.1%
123000011
 
< 0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%
12050001
 
< 0.1%

18.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct606
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219674.648
Minimum0
Maximum1245000
Zeros2001
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:53.964138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q147000
median138000
Q3300000
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)253000

Descriptive statistics

Standard deviation263819.5034
Coefficient of variation (CV)1.200955621
Kurtosis3.810735299
Mean219674.648
Median Absolute Deviation (MAD)93000
Skewness1.973978459
Sum5063939986
Variance6.960073037 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:54.173296image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02001
 
8.7%
450001044
 
4.5%
170000685
 
3.0%
30000646
 
2.8%
160000607
 
2.6%
157000600
 
2.6%
60000578
 
2.5%
63000516
 
2.2%
72000447
 
1.9%
62000387
 
1.7%
Other values (596)15541
67.4%
ValueCountFrequency (%)
02001
8.7%
5831
 
< 0.1%
10001
 
< 0.1%
22501
 
< 0.1%
30002
 
< 0.1%
35001
 
< 0.1%
60661
 
< 0.1%
700018
 
0.1%
75002
 
< 0.1%
80006
 
< 0.1%
ValueCountFrequency (%)
124500049
 
0.2%
1240000259
1.1%
123500056
 
0.2%
12320001
 
< 0.1%
123000011
 
< 0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%
12050001
 
< 0.1%

19.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct557
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219916.5133
Minimum0
Maximum1245000
Zeros2005
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:54.388627image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q147000
median138000
Q3302000
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)255000

Descriptive statistics

Standard deviation264118.2403
Coefficient of variation (CV)1.200993215
Kurtosis3.815611282
Mean219916.5133
Median Absolute Deviation (MAD)93000
Skewness1.974896603
Sum5069515465
Variance6.975844486 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:54.603503image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02005
 
8.7%
450001048
 
4.5%
170000685
 
3.0%
30000653
 
2.8%
160000608
 
2.6%
157000598
 
2.6%
60000583
 
2.5%
63000521
 
2.3%
72000445
 
1.9%
62000388
 
1.7%
Other values (547)15518
67.3%
ValueCountFrequency (%)
02005
8.7%
24161
 
< 0.1%
25501
 
< 0.1%
26831
 
< 0.1%
30002
 
< 0.1%
42661
 
< 0.1%
50001
 
< 0.1%
52501
 
< 0.1%
700021
 
0.1%
80006
 
< 0.1%
ValueCountFrequency (%)
124500049
 
0.2%
1240000261
1.1%
123500060
 
0.3%
12320001
 
< 0.1%
123000011
 
< 0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%
12069331
 
< 0.1%

20.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct580
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219997.8319
Minimum0
Maximum1245000
Zeros2001
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:54.803235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q147000
median138000
Q3303250
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)256250

Descriptive statistics

Standard deviation264333.2397
Coefficient of variation (CV)1.201526567
Kurtosis3.820327964
Mean219997.8319
Median Absolute Deviation (MAD)93000
Skewness1.975745535
Sum5071390021
Variance6.987206158 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:55.022233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02001
 
8.7%
450001033
 
4.5%
170000685
 
3.0%
30000669
 
2.9%
160000607
 
2.6%
157000597
 
2.6%
60000584
 
2.5%
63000523
 
2.3%
72000445
 
1.9%
62000386
 
1.7%
Other values (570)15522
67.3%
ValueCountFrequency (%)
02001
8.7%
6001
 
< 0.1%
7502
 
< 0.1%
8001
 
< 0.1%
10001
 
< 0.1%
13661
 
< 0.1%
15001
 
< 0.1%
17001
 
< 0.1%
21001
 
< 0.1%
30002
 
< 0.1%
ValueCountFrequency (%)
124500050
 
0.2%
1240000262
1.1%
123500062
 
0.3%
12320001
 
< 0.1%
123000011
 
< 0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%
12050001
 
< 0.1%

21.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct574
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219574.6087
Minimum0
Maximum1245000
Zeros2040
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:55.223653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145000
median138000
Q3304000
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)259000

Descriptive statistics

Standard deviation264675.1815
Coefficient of variation (CV)1.205399764
Kurtosis3.801644694
Mean219574.6087
Median Absolute Deviation (MAD)93000
Skewness1.97226044
Sum5061633879
Variance7.005295172 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:55.463578image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02040
 
8.8%
450001007
 
4.4%
170000687
 
3.0%
30000668
 
2.9%
160000608
 
2.6%
157000598
 
2.6%
60000584
 
2.5%
63000525
 
2.3%
72000444
 
1.9%
62000388
 
1.7%
Other values (564)15503
67.3%
ValueCountFrequency (%)
02040
8.8%
15004
 
< 0.1%
20001
 
< 0.1%
21331
 
< 0.1%
22501
 
< 0.1%
29161
 
< 0.1%
30004
 
< 0.1%
37503
 
< 0.1%
45007
 
< 0.1%
50001
 
< 0.1%
ValueCountFrequency (%)
124500050
 
0.2%
1240000260
1.1%
123500062
 
0.3%
12320001
 
< 0.1%
123000011
 
< 0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12160001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%

22.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct572
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218354.296
Minimum0
Maximum1245000
Zeros2167
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:55.668315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145000
median138000
Q3300500
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)255500

Descriptive statistics

Standard deviation263562.8469
Coefficient of variation (CV)1.207042186
Kurtosis3.767672884
Mean218354.296
Median Absolute Deviation (MAD)93000
Skewness1.9626287
Sum5033503232
Variance6.946537425 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:55.883723image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02167
 
9.4%
45000965
 
4.2%
170000687
 
3.0%
30000666
 
2.9%
160000608
 
2.6%
157000591
 
2.6%
60000583
 
2.5%
63000526
 
2.3%
72000446
 
1.9%
62000397
 
1.7%
Other values (562)15416
66.9%
ValueCountFrequency (%)
02167
9.4%
5001
 
< 0.1%
7501
 
< 0.1%
15003
 
< 0.1%
20001
 
< 0.1%
27331
 
< 0.1%
30003
 
< 0.1%
32001
 
< 0.1%
33331
 
< 0.1%
375010
 
< 0.1%
ValueCountFrequency (%)
124500050
 
0.2%
1240000239
1.0%
123500062
 
0.3%
12320002
 
< 0.1%
123000011
 
< 0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%
12050001
 
< 0.1%

23.0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct565
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217715.2132
Minimum0
Maximum1245000
Zeros2288
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size180.2 KiB
2021-07-01T20:52:56.088604image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145000
median138000
Q3300000
95-th percentile791000
Maximum1245000
Range1245000
Interquartile range (IQR)255000

Descriptive statistics

Standard deviation262643.9051
Coefficient of variation (CV)1.206364504
Kurtosis3.769510864
Mean217715.2132
Median Absolute Deviation (MAD)93000
Skewness1.959192848
Sum5018771095
Variance6.898182089 × 1010
MonotonicityNot monotonic
2021-07-01T20:52:56.303352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02288
 
9.9%
45000952
 
4.1%
170000688
 
3.0%
30000673
 
2.9%
160000608
 
2.6%
157000590
 
2.6%
60000584
 
2.5%
63000520
 
2.3%
72000443
 
1.9%
62000385
 
1.7%
Other values (555)15321
66.5%
ValueCountFrequency (%)
02288
9.9%
7502
 
< 0.1%
9001
 
< 0.1%
15001
 
< 0.1%
22501
 
< 0.1%
24163
 
< 0.1%
25001
 
< 0.1%
27331
 
< 0.1%
29161
 
< 0.1%
30001
 
< 0.1%
ValueCountFrequency (%)
124500050
 
0.2%
1240000230
1.0%
123500062
 
0.3%
12320002
 
< 0.1%
123000011
 
< 0.1%
12250001
 
< 0.1%
12200001
 
< 0.1%
12150001
 
< 0.1%
12100001
 
< 0.1%
12050001
 
< 0.1%

Interactions

2021-07-01T20:50:46.703617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:46.933790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:47.128463image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:47.318451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:47.503422image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:47.693799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:47.923796image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:48.193657image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:48.393577image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:48.713695image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:48.963431image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:49.168673image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:49.378565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:49.588486image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:49.773550image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:49.963539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:50.148532image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:50.348728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:50.523563image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:50.718591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:50.903674image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:51.093740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:51.278448image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:51.473730image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:51.803728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:52.053586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:52.283814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:52.513788image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:52.733717image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:52.923615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:53.113699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:53.393668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:53.573430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:53.758689image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:53.949419image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:54.208814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:54.398600image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:54.573776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:54.758591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:54.938575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:55.113420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:55.295503image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:55.495838image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:55.688447image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:55.863728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:56.038887image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:56.213477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:56.396889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:56.588830image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:56.763616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:56.948474image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:57.128402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:57.303818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:57.483772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:57.668566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:57.853654image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:58.033505image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:58.213454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:58.403732image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:58.573442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:58.763840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:59.073886image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:59.254777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:59.458549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:59.653826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:50:59.838850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:00.028713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:00.283451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:00.508403image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:00.699874image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:00.873644image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:01.043871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:01.223733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:01.413821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:01.599963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:01.783484image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:01.963461image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:02.148666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:02.323623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:02.503817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:02.693534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:02.868845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:03.043598image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:03.223624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:03.418599image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:03.593676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:03.783417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:03.963525image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:04.153572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:04.343735image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:04.528421image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:04.733455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:04.923775image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:05.118689image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:05.303768image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:05.483551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:05.663509image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:05.853415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:06.193794image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:06.378595image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:06.553657image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:06.748772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:06.933418image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:07.113684image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:07.293841image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:07.478654image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:07.653588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:07.843652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:08.023565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:08.193834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:08.383802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:08.553846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:08.733412image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:08.918580image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:09.098326image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:09.273760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:09.453707image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:09.638470image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:09.813387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:09.993692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:10.178588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:10.373621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:10.553726image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:10.738685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:10.913723image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:11.093734image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:11.273572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:11.473680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:11.653750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:11.833398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:12.013523image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:12.193397image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:12.378447image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:51:12.563648image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-07-01T20:52:30.978416image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:31.203654image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:31.426816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:31.633729image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:31.823623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:31.998292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:32.193629image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:32.403468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:32.603355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:32.813392image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:33.013453image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:33.323186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:33.653272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:33.845240image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:34.043544image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:34.228329image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:34.403377image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:34.579215image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:34.753526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:34.963740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:35.163538image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:35.353427image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:35.523507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:35.723535image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:35.928692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:36.138606image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:36.323363image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:36.528315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:36.713659image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:36.913370image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:37.123672image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:37.343412image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:37.533728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:37.733775image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:37.935451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:38.128674image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:38.363672image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:38.563315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:38.743243image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:38.928380image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:39.133526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:39.328526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-01T20:52:39.513755image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-07-01T20:52:56.553380image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-01T20:52:56.923759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-01T20:52:57.293325image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-01T20:52:57.688344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-01T20:52:58.018498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-01T20:52:40.463535image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-01T20:52:41.688390image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

FechaRecursoCódigo RecursoTipo GeneraciónCódigo Agente0.01.02.03.04.05.06.07.08.09.010.011.012.013.014.015.016.017.018.019.020.021.022.023.0
02019-01-01 00:00:00ALBANALBGHIDRAULICAEPSG145000.0145000.0145000.0145000.0145000.0145000.0145000.0145000.0145000.0145000.0145000.0145000.0145000.0145000.0145000.0145000.0145000.0145000.0145000.0145000.0145000.0145000.0145000.0145000.0
12019-01-01 00:00:00AMOYA LA ESPERANZAMOY1HIDRAULICAISGG40000.040000.040000.040000.040000.040000.040000.040000.040000.040000.040000.040000.040000.040000.040000.040000.040000.040000.040000.040000.040000.040000.040000.040000.0
22019-01-01 00:00:00BETANIACHBGHIDRAULICAENDG453000.0453000.0453000.0453000.0453000.0453000.0453000.0453000.0453000.0453000.0453000.0453000.0453000.0453000.0453000.0453000.0453000.0453000.0453000.0453000.0453000.0453000.0453000.0453000.0
32019-01-01 00:00:00CALIMACLMGHIDRAULICAEPSG132000.0132000.0132000.0132000.0132000.0132000.0132000.0132000.0132000.0132000.0132000.0132000.0132000.0132000.0132000.0132000.0132000.0132000.0132000.0132000.0132000.0132000.0132000.0132000.0
42019-01-01 00:00:00CARLOS LLERASCLL1HIDRAULICAHDPG18000.018000.018000.018000.018000.018000.018000.018000.018000.018000.018000.018000.018000.018000.018000.018000.018000.018000.024666.043000.043000.043000.043000.043000.0
52019-01-01 00:00:00CHIVORCHVRHIDRAULICACHVG1000000.01000000.01000000.01000000.01000000.01000000.01000000.01000000.01000000.01000000.01000000.01000000.01000000.01000000.01000000.01000000.01000000.01000000.01000000.01000000.01000000.01000000.01000000.01000000.0
62019-01-01 00:00:00CUCUANACUC1HIDRAULICAEPSG10000.010000.010000.010000.010000.010000.010000.010000.010000.010000.010000.010000.010000.010000.010000.010000.010000.010000.010000.010000.010000.010000.010000.010000.0
72019-01-01 00:00:00DARIO VALENCIA SAMPERDVS1HIDRAULICAENDG150000.0150000.0150000.0150000.0150000.0150000.0150000.0150000.0150000.0150000.0150000.0150000.0150000.0150000.0150000.0150000.0150000.0150000.0150000.0150000.0150000.0150000.0150000.0150000.0
82019-01-01 00:00:00EL QUIMBOQUI1HIDRAULICAENDG386000.0386000.0386000.0386000.0386000.0386000.0386000.0386000.0386000.0386000.0386000.0386000.0386000.0386000.0386000.0386000.0386000.0386000.0386000.0386000.0386000.0386000.0386000.0386000.0
92019-01-01 00:00:00ESMERALDAESMRHIDRAULICAEPMG30000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.0

Last rows

FechaRecursoCódigo RecursoTipo GeneraciónCódigo Agente0.01.02.03.04.05.06.07.08.09.010.011.012.013.014.015.016.017.018.019.020.021.022.023.0
230422019-12-31 00:00:00TERMODORADA 1TDR1TERMICAEPMG22000.022000.022000.022000.022000.022000.022000.022000.022000.022000.022000.022000.022000.022000.022000.022000.022000.022000.022000.022000.022000.022000.022000.022000.0
230432019-12-31 00:00:00TERMOEMCALI 1TEC1TERMICATEMG213000.0213000.0213000.0213000.0213000.0213000.0213000.0213000.0213000.0213000.0213000.0213000.0213000.0213000.0213000.0213000.0213000.0213000.0213000.0213000.0213000.0213000.0213000.0213000.0
230442019-12-31 00:00:00TERMONORTETRN1TERMICATMNG81000.081000.081000.081000.081000.081000.081000.081000.081000.081000.081000.081000.081000.081000.081000.081000.081000.081000.081000.081000.081000.081000.081000.081000.0
230452019-12-31 00:00:00TERMOSIERRABTSR1TERMICAEPMG205000.0205000.0205000.0205000.0205000.0205000.0205000.0205000.0205000.0205000.0200000.0200000.0200000.0200000.0200000.0200000.0200000.0200000.0200000.0205000.0205000.0205000.0205000.0205000.0
230462019-12-31 00:00:00TERMOVALLE 1TVL1TERMICATMVG200000.0200000.0200000.0200000.0200000.0200000.0200000.0200000.0200000.0200000.0200000.0200000.0200000.0200000.0200000.0200000.0200000.0200000.0200000.0200000.0200000.0200000.0200000.0200000.0
230472019-12-31 00:00:00TERMOYOPAL 2TYP2TERMICATYPG30000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.030000.0
230482019-12-31 00:00:00ZIPAEMG 2ZPA2TERMICAENDG35000.035000.035000.035000.035000.035000.035000.035000.035000.035000.035000.035000.035000.035000.035000.035000.035000.035000.035000.035000.035000.035000.035000.035000.0
230492019-12-31 00:00:00ZIPAEMG 3ZPA3TERMICAENDG0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
230502019-12-31 00:00:00ZIPAEMG 4ZPA4TERMICAENDG59000.059000.059000.059000.059000.059000.059000.059000.059000.059000.059000.059000.059000.059000.059000.059000.059000.059000.059000.059000.059000.059000.059000.059000.0
230512019-12-31 00:00:00ZIPAEMG 5ZPA5TERMICAENDG60000.060000.060000.060000.060000.060000.060000.060000.060000.060000.060000.060000.060000.060000.060000.060000.060000.060000.060000.060000.060000.060000.060000.060000.0